Motivation¶
- Weeks Lab GitHub has many highly specialized scripts.
- plotting
- filtering
- file conversion
- clipping structure cassettes
- analysis
- If you know what you want ahead of time, you can create a nice figure.
- Data exploration is difficult because we have too many scripts and create too many files.
Solution: Jupyter Notebooks and plotmapper.py¶
Jupyter Notebooks come installed with Anaconda, and are accessible on Longleaf through OpenOnDemand.
plotmapper.py can be found in the JNBTools repo on Github.
Jupyter Notebooks¶
- Really nice for anybody doing data analysis.
- Makes your analysis easily reproducible.
- Do everything in one place.
- Text, code, and figures, all together.
- Exports to PDF, HTML, and HTML slide shows for sharing.
- This presentation is a Jupyter Notebook.
plotmapper.py¶
- Makes it easy:
- Filtering data.
- Analyzing data.
- Plotting data.
Filtering:¶
- Fits data by sequence
- Done automatically any time you want to compare data.
- no more clipping/padding for structure cassettes
- not limited to structure cassettes
- Filter by any column in your data tables
- Statistic, Z-score, Percentile, Deletion Rate, Read Depth, etc.
- Filter by contact distances
- Filter by 3-D distances
Plotting:¶
Plotmapper.py includes a variety of tools for plotting:
- ShapeMapper QC data:
- mutations per molecule
- read length distribution
- 1-D Reactivity data:
- SHAPE-MaP
- DANCE-MaP
- 2-D correlation data:
- Rings
- Pairs
- Deletions
- DANCE-MaP
Installation is simple¶
I'm happy to help with this. Instructions are on the GitHub page.
Notebook Setup¶
The first code cell of a notebook should define defaults and load in modules
For high-level plotting functions, you only need to import plotmapper.
For this demonstration, I also need matplotlib.pyplot.
In [1]:
# Display plots in-line
%matplotlib inline
# import modules
import plotmapper as MaP
import matplotlib.pyplot as plt
Initializing MaP.Sample¶
MaP.Sample is the core object in this package. For each MaP experimental sample, it holds the following information.
- Sample name
- Base-pairing information (.ct)
- Secondary Structure (.xrna, .varna, .cte, .nsd)
- Tertiary Structure (.pdb)
- requires PDB entry name
- ShapeMapper Log file
- ShapeMapper Profile
- RingMapper data
- PairMapper data
- DANCE-MaP prefix:
- Finds: reactivities, pairs, rings, allcorrs, and ct files if present.
- SHAPE-JuMP deletions data
- requires a reference fasta file
In [2]:
example1 = MaP.Sample(sample="example1",
profile = 'data/example1_rnasep_profile.txt',
ct = 'data/RNaseP.ct',
ss = 'data/RC_CRYSTAL_STRUCTURE.xrna',
rings = 'data/example1-rnasep.corrs',
pairs = 'data/example1-rnasep-pairmap.txt',
log = 'data/example1_shapemapper_log.txt',
dance_prefix = 'data/example1_rnasep',
deletions = 'data/example-rnasep-deletions.txt',
fasta = 'data/RNaseP-noSC.fasta',
pdb = 'data/3dhs_Correct.pdb',
pdb_name = '3dhs')
example2 = MaP.Sample(sample="example2",
profile = 'data/example2_rnasep_profile.txt',
ct = 'data/RNaseP.ct',
ss = 'data/RC_CRYSTAL_STRUCTURE.xrna',
rings = 'data/example2-rnasep.corrs',
pairs = 'data/example2-rnasep-pairmap.txt',
log = 'data/example2_shapemapper_log.txt',
dance_prefix = 'data/example2_rnasep',
deletions = 'data/example-rnasep-deletions.txt',
fasta = 'data/RNaseP-noSC.fasta',
pdb = 'data/3dhs_Correct.pdb',
pdb_name = '3dhs')
In [3]:
example3 = MaP.Sample(sample="example3",
profile = 'data/example3_rnasep_profile.txt',
ct = 'data/RNaseP.ct',
ss = 'data/RC_CRYSTAL_STRUCTURE.xrna',
rings = 'data/example3-rnasep.corrs',
pairs = 'data/example3-rnasep-pairmap.txt',
log = 'data/example3_shapemapper_log.txt',
dance_prefix = 'data/example3_rnasep',
deletions = 'data/example-rnasep-deletions.txt',
fasta = 'data/RNaseP-noSC.fasta',
pdb = 'data/3dhs_Correct.pdb',
pdb_name = '3dhs')
example4 = MaP.Sample(sample="example4",
profile = 'data/example4_rnasep_profile.txt',
ct = 'data/RNaseP.ct',
ss = 'data/RC_CRYSTAL_STRUCTURE.xrna',
rings = 'data/example4-rnasep.corrs',
pairs = 'data/example4-rnasep-pairmap.txt',
log = 'data/example4_shapemapper_log.txt',
dance_prefix = 'data/example4_rnasep',
deletions = 'data/example-rnasep-deletions.txt',
fasta = 'data/RNaseP-noSC.fasta',
pdb = 'data/3dhs_Correct.pdb',
pdb_name = '3dhs')
High-level plotting functions¶
- Single sample plotting:
- sample.make_plot(arguments)
- Multi-sample plotting:
- MaP.array_plot(samples, arguments)
- Plot can be:
- log_qc
- shapemapper
- skyline
- dance_skyline
- heatmap
- ap
- ss
- 3d
ShapeMapper QC¶
- make_log_qc (high-level function)
- plot_log_MutsPerMol
- set_log_MutsPerMol
- make_log_MutsPerMol
- plot_log_ReadLength
- set_log_ReadLength
- make_log_ReadLength
- get_boxplot_data
- plot_boxplot
- array_qc
In [4]:
example2.make_log_qc();
In [5]:
MaP.array_qc([example1, example2, example3, example4]);
Linear Regressions¶
- plot_regression
In [6]:
fig, ax = plt.subplots(1,2, figsize=(14,7))
example2.plot_regression(example1, ax=ax[0])
example4.plot_regression(example3, ax=ax[1], colorby="nucleotide")
Classic ShapeMapper Plots¶
- make_shapemapper
- plot_sm_profile
- plot_sm_depth
- plot_sm_rates
In [7]:
example2.plot_sm_profile();
In [8]:
example2.plot_sm_rates();
In [9]:
example2.plot_sm_depth();
In [10]:
example2.make_shapemapper();
Skyline Plots¶
- make_skyline
- make_dance_skyline
- get_skyline_figsize
- plot_skyline
- plot_sequence
- array_skyline
In [11]:
example2.make_skyline();
In [12]:
MaP.array_skyline([example1, example2, example3, example4]);
In [13]:
example2.make_dance_skyline();
Colorbars¶
The plots I'll be showing don't have colorbars yet. To get a stand-alone colorbar, use the view_colormap() function:
In [15]:
MaP.view_colormap("pairs")
MaP.view_colormap("rings")
MaP.view_colormap("deletions")
MaP.view_colormap("deletions", metric="Distance")
Heatmap and Contour Plots¶
- make_heatmap
- get_distance_matrix (This is not speedy yet for contact distances.)
- plot_contour_distances
- plot_heatmap_data
In [16]:
fig, ax = plt.subplots(1, 2, figsize=(14, 7))
example2.make_heatmap("deletions", "pdb", ax=ax[0])
example2.make_heatmap("deletions", "ct", ax=ax[1]);
Arc Plots¶
- make_ap
- add_arc
- get_ap_figsize
- set_ap
- plot_ap_ct
- plot_ap_ctcompare
- plot_ap_profile
- plot_ap_data
- array_ap
- make_ap
In [17]:
example2.make_ap(attribute="deletions", Percentile=0.95);
In [18]:
MaP.array_ap([example1, example2, example3, example4], attribute="rings", cdAbove=15);
Secondary Structure¶
- make_ss
- set_ss
- plot_ss_structure
- plot_ss_sequence
- plot_ss_positions
- set_3d_distances (if coloring by 3d distance)
- plot_ss_data
- array_ss
- make_ss
In [19]:
example2.make_ss(attribute="rings");
In [20]:
MaP.array_ss([example1, example2, example3, example4], attribute="pairs");
3D molecule interactive plots¶
Controls:
- click and drag to rotate
- mouse scroll or right click to zoom
- 3rd mouse button and drag to pan
In [22]:
example2.make_3d(attribute="deletions", metric="Distance", Percentile=0.99)
Out[22]:
In [23]:
MaP.array_3d([example1, example2, example3, example4], attribute="rings", Statistic=15)
Out[23]:
Review¶
PlotMapper and Jupyter Notebooks provides a fast and easy way to explore MaP and JuMP data sets.
- Quality contol
- Skylines
- Linear Regression scatter plots
- Arc Plots
- Heatmaps
- Secondary Structure
- 3D structure
- etc.
Still left to do:¶
- Improve look and readability of some figures.
- Add functionality that is commonly used in lab, but not by me.
- Create a command line interface.
- Taking requests.
What I need from the Weeks Lab¶
- Testing, figuring out when my code fails.
- Ideas for improving the look and readability of plots.
- Plotmapper is very extensible. New functions should be added to it instead of creating new scripts.
In [ ]: